Perception-Based Analysis: An innovative approach for brand positioning assessment

Abstract

The work discusses the Perception-Based Analysis (PBA) and its adequacy for evaluating brands positioning from the point of view of the consumers. PBA is a relatively new post hoc segmentation method, based on a topology representing neural network, able to identify homogeneous segments of perceptions in an indiscriminate mass of data. The Neural Gas algorithm was used to find clusters in a sample of 376 students who evaluated the Nike brand across 42 items of Brand Personality Scale. Discriminant Analysis was performed to check the ability of PBA to form homogeneous segments, and an Exploratory/Confirmatory Factor Analysis (E/CFA) was carried out to confirm the validation. Five prototypes of perception were identified and described according to the importance respondents put on brand attributes. Four (among five) prototypes demonstrate a significant relationship (positive or inverse) with two or more dimensions of brand perception. Homogeny of the segments was attested, both, by Discriminant and by the E/CFA. Results suggest that PBA was both valid and reliable for capturing output brand positioning, once it succeeded in performing two important segmentation tasks: (a) identifying clusters relatively homogeneous in terms of brand perception, and (b) portraying the general opinion prevailing in every group of consumers about the brand assessed. © 2010 Macmillan Publishers Ltd.